Parametric study designs can reveal information about the relationship between a study parameter (e.g., word presentation rate) and regional cerebral blood flow (rCBF) in functional imaging. The brain's responses in relation to study parameters might be nonlinear, therefore the (linear) correlation coefficient as often used in the analysis of parametric studies might not be a proper characteriz...

This paper examines a weighted version of the quantile regression estimator defined by Koenker and Bassett (1978), adjusted to the case of nonlinear longitudinal data. Different weights are used and compared by computer simulation using a four-parameter logistic growth function and error terms following an AR(1) model. It is found that the estimator is performing quite well, especially for the ...

In this paper, we study parametric nonlinear regression under the Harris recurrent Markov chain framework. We first consider the nonlinear least squares estimators of the parameters in the homoskedastic case, and establish asymptotic theory for the proposed estimators. Our results show that the convergence rates for the estimators rely not only on the properties of the nonlinear regression func...

Analyzing a nonlinear system on high dimensions is not simple, especially with the practical applications. Since the prediction of the output from the dynamical system can be formulated as a regression problem, many previous attempts have been made to solve this regression. With the assumption of nonlinearity in the system, we attempt to apply encoding method, such as sparse coding or local coo...

Under assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression problem. We present a method for estimating code multipath error of GPS ground fixed stations. By formula...

Survival analysismethods deal with a type of data, which is waiting time till occurrence of an event. One commonmethod to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that effect ...

Motivated by the desire to extend fast randomized techniques to nonlinear lp regression, we consider a class of structured regression problems. These problems involve Vandermonde matrices which arise naturally in various statistical modeling settings, including classical polynomial fitting problems, additive models and approximations to recently developed randomized techniques for scalable kern...

Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than pre-programme...

with xi ∈ Rk, θ = (θ0, θ1, . . . , θp)′ ∈ Θ (compact in Rp+1), where g(x, θ) = θ0 + g̃(x, θ1, . . . , θp) is continuous, twice differentiable in θ and monotone in components of θ. Following Gutenbrunner and Jurečková (1992) and Jurečková and Procházka (1994), we introduce regression rank scores for model (1), and prove their asymptotic properties under some regularity conditions. As an applicati...